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One of the goals of theSuperconducting Quantum Materials and Systems Centeris to build a beyond-state-of-the-art quantum computer based on superconducting technologies.The center also will develop new quantum sensors, which could lead to the discovery of the nature of dark matter and other elusive subatomic particles.

The U.S. Department of Energys Fermilab has been selected to lead one of five national centers to bring about transformational advances in quantum information science as a part of the U.S. National Quantum Initiative, announced the White House Office of Science and Technology Policy, the National Science Foundation and the U.S. Department of Energy today.

The initiative provides the newSuperconducting Quantum Materials and Systems Centerfunding with the goal of building and deploying a beyond-state-of-the-art quantum computer based on superconducting technologies. The center also will develop new quantum sensors, which could lead to the discovery of the nature of dark matter and other elusive subatomic particles. Total planned DOE funding for the center is $115 million over five years, with $15 million in fiscal year 2020 dollars and outyear funding contingent on congressional appropriations. SQMS will also receive an additional $8 million in matching contributions from center partners.

The SQMS Center is part of a $625 million federal program to facilitate and foster quantum innovation in the United States. The 2018 National Quantum Initiative Act called for a long-term, large-scale commitment of U.S. scientific and technological resources to quantum science.

The revolutionary leaps in quantum computing and sensing that SQMS aims for will be enabled by a unique multidisciplinary collaboration that includes 20 partners national laboratories, academic institutions and industry. The collaboration brings together world-leading expertise in all key aspects: from identifying qubits quality limitations at the nanometer scale to fabrication and scale-up capabilities into multiqubit quantum computers to the exploration of new applications enabled by quantum computers and sensors.

The breadth of the SQMS physics, materials science, device fabrication and characterization technology combined with the expertise in large-scale integration capabilities by the SQMS Center is unprecedented for superconducting quantum science and technology, said SQMS Deputy Director James Sauls of Northwestern University. As part of the network of National QIS Research centers, SQMS will contribute to U.S. leadership in quantum science for the years to come.

SQMS researchers are developing long-coherence-time qubits based on Rigetti Computings state-of-the-art quantum processors. Image: Rigetti Computing

At the heart of SQMS research will be solving one of the most pressing problems in quantum information science: the length of time that a qubit, the basic element of a quantum computer, can maintain information, also called quantum coherence. Understanding and mitigating sources of decoherence that limit performance of quantum devices is critical to engineering in next-generation quantum computers and sensors.

Unless we address and overcome the issue of quantum system decoherence, we will not be able to build quantum computers that solve new complex and important problems. The same applies to quantum sensors with the range of sensitivity needed to address long-standing questions in many fields of science, said SQMS Center Director Anna Grassellino of Fermilab. Overcoming this crucial limitation would allow us to have a great impact in the life sciences, biology, medicine, and national security, and enable measurements of incomparable precision and sensitivity in basic science.

The SQMS Centers ambitious goals in computing and sensing are driven by Fermilabs achievement of world-leading coherence times in components called superconducting cavities, which were developed for particle accelerators used in Fermilabs particle physics experiments. Researchers have expanded the use of Fermilab cavities into the quantum regime.

We have the most coherent by a factor of more than 200 3-D superconducting cavities in the world, which will be turned into quantum processors with unprecedented performance by combining them with Rigettis state-of-the-art planar structures, said Fermilab scientist Alexander Romanenko, SQMS technology thrust leader and Fermilab SRF program manager. This long coherence would not only enable qubits to be long-lived, but it would also allow them to be all connected to each other, opening qualitatively new opportunities for applications.

The SQMS Centers goals in computing and sensing are driven by Fermilabs achievement of world-leading coherence times in components called superconducting cavities, which were developed for particle accelerators used in Fermilabs particle physics experiments. Photo: Reidar Hahn, Fermilab

To advance the coherence even further, SQMS collaborators will launch a materials-science investigation of unprecedented scale to gain insights into the fundamental limiting mechanisms of cavities and qubits, working to understand the quantum properties of superconductors and other materials used at the nanoscale and in the microwave regime.

Now is the time to harness the strengths of the DOE laboratories and partners to identify the underlying mechanisms limiting quantum devices in order to push their performance to the next level for quantum computing and sensing applications, said SQMS Chief Engineer Matt Kramer, Ames Laboratory.

Northwestern University, Ames Laboratory, Fermilab, Rigetti Computing, the National Institute of Standards and Technology, the Italian National Institute for Nuclear Physics and several universities are partnering to contribute world-class materials science and superconductivity expertise to target sources of decoherence.

SQMS partner Rigetti Computing will provide crucial state-of-the-art qubit fabrication and full stack quantum computing capabilities required for building the SQMS quantum computer.

By partnering with world-class experts, our work will translate ground-breaking science into scalable superconducting quantum computing systems and commercialize capabilities that will further the energy, economic and national security interests of the United States, said Rigetti Computing CEO Chad Rigetti.

SQMS will also partner with the NASA Ames Research Center quantum group, led by SQMS Chief Scientist Eleanor Rieffel. Their strengths in quantum algorithms, programming and simulation will be crucial to use the quantum processors developed by the SQMS Center.

The Italian National Institute for Nuclear Physics has been successfully collaborating with Fermilab for more than 40 years and is excited to be a member of the extraordinary SQMS team, said INFN President Antonio Zoccoli. With its strong know-how in detector development, cryogenics and environmental measurements, including the Gran Sasso national laboratories, the largest underground laboratory in the world devoted to fundamental physics, INFN looks forward to exciting joint progress in fundamental physics and in quantum science and technology.

Fermilab is excited to host this National Quantum Information Science Research Center and work with this extraordinary network of collaborators, said Fermilab Director Nigel Lockyer. This initiative aligns with Fermilab and its mission. It will help us answer important particle physics questions, and, at the same time, we will contribute to advancements in quantum information science with our strengths in particle accelerator technologies, such as superconducting radio-frequency devices and cryogenics.

We are thankful and honored to have this unique opportunity to be a national center for advancing quantum science and technology, Grassellino said. We have a focused mission: build something revolutionary. This center brings together the right expertise and motivation to accomplish that mission.

The Superconducting Quantum Materials and Systems Center at Fermilab is supported by the DOE Office of Science.

Fermilab is supported by the Office of Science of the U.S. Department of Energy. The Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit science.energy.gov.

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Fermilab to lead $115 million National Quantum Information Science Research Center to build revolutionary quantum computer with Rigetti Computing,...

Tufts is joining a major U.S. Department of Energy (DOE) funded center called the Quantum Systems Accelerator (QSA), led by Lawrence Berkeley National Laboratory. The center hopes to create the next generation of quantum computers and apply them to the study of some of the most challenging problems in physics, chemistry, materials science, and more.

The QSA is one of five new DOE Quantum Information Science research centers announced on Aug. 26, and will be funded with $115 million over five years, supporting dozens of scientists at 15 institutions.

Peter Love, an associate professor of physics, will lead Tufts participation in the project. We have long been interested in using quantum computers for calculations in physics and chemistry, said Love.

A large-scale quantum computer would be a very powerful instrument for studying everything from the structure of large molecules to the nature and behavior of subatomic particles, he said. The only difficulty is that the quantum computers we need dont exist yet.

Quantum computers employ a fundamentally different approach to computing than those existing now, using quantum states of atoms, ions, light, quantum dots or superconducting circuits to store information.

The QSA will bring together world-class researchers and facilities to develop quantum systems that could significantly exceed the capability of todays computers. Multidisciplinary teams across all the institutions will work toward advancing qubit technologythe manner and materials in which information is stored in a quantum state, and other components of quantum computers.

Loves research will focus on developing simulation algorithms in areas such as particle and nuclear physics, which will be run by the new quantum computers. It is important to work hard on the algorithms now, so we are ready when the hardware appears, he said. Love is also part of a National Science Foundation-funded effort to develop a quantum computer and applications to run on it.

Quantum computing is an important and growing area of research at Tufts. Tom Vandervelde, an associate professor in electrical and computer engineering, Luke Davis, an assistant professor of chemistry, and Cristian Staii, an associate professor of physics, are exploring new materials capable of storing qubits.

Philip Shushkov, Charles W. Fotis Assistant Professor of Chemistry, has research focused on theoretical modeling of qubit materials, while Misha Kilmer, William Walker Professor of Mathematics, and Xiaozhe Hu, associate professor of mathematics, study quantum-inspired algorithms relevant to their research in linear algebra. Bruce Boghosian, professor of mathematics, also made some fundamental contributions to quantum simulation in the late 1990s.

Mike Silver can be reached at mike.silver@tufts.edu.

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Tufts Joins Major Effort to Build the Next Generation of Quantum Computers - Tufts Now

The Drake equation is one of the more famous reckonings in science. It calculates the likelihood that we are not alone in the universe by estimating the number of other intelligent civilizations in our galaxy that might exist now.

Some of the terms in this equation are well known or becoming better understood, such as the number of stars in our galaxy and the proportion that have planets in the habitable zone. But others are unknown, such as the proportion of planets that develop intelligent life; and some may never be known such as the proportion that destroy themselves before they can be discovered.

Nevertheless, the Drake equation allows scientists to place important bounds on the numbers of intelligent civilizations that might be out there.

However, there is another sense in which humanity could be linked with an alien intelligenceour world may just be a simulation inside a massively powerful supercomputer run by such a species. Indeed, various scientists, philosophers and visionaries have said that the probability of such a scenario could be close to one. In other words, we probably are living in a simulation.

The accuracy of these claims is somewhat controversial. So a better way to determine the probability that we live in a simulation would be much appreciated.

Enter Alexandre Bibeau-Delisle and Gilles Brassard at the University of Montreal in Canada. These researchers have derived a Drake-like equation that calculates the chances that we live in a computer simulation. And the results throw up some counterintuitive ideas that are likely to change the way we think about simulations, how we might determine whether we are in one and whether we could ever escape.

Bibeau-Delisle and Brassard begin with a fundamental estimate of the computing power available to create a simulation. They say, for example, that a kilogram of matter, fully exploited for computation, could perform 10^50 operations per second.

By comparison, the human brain, which is also kilogram-sized, performs up to 10^16 operations per second. It may thus be possible for a single computer the mass of a human brain to simulate the real-time evolution of 1.4 10^25 virtual brains, they say.

In our society, a significant number of computers already simulate entire civilizations, in games such as Civilization VI, Hearts of Iron IV, Humankind and so. So it may be reasonable to assume that in a sufficiently advanced civilization, individuals will be able to run games that simulate societies like ours, populated with sentient conscious beings.

So an interesting question is this: of all the sentient beings in existence, what fraction are likely to be simulations? To derive the answer, Bibeau-Delisle and Brassard start with the total number of real sentient beings NRe, multiply that by the fraction with access to the necessary computing power fCiv; multiply this by the fraction of that power that is devoted to simulating consciousness fDed (because these beings are likely to be using their computer for other purposes too); and then multiply this by the number of brains they could simulate Rcal.

The resulting equation is this, where fSim is the fraction of simulated brains:

Here RCal is the huge number of brains that fully exploited matter should be able to simulate.

The sheer size of this number, ~10^25, pushes Bibeau-Delisle and Brassard towards an inescapable conclusion. It is mathematically inescapable from [the above] equation and the colossal scale of RCal that fSim 1 unless fCiv fDed 0, they say.

So there are two possible outcomes. Either we live in a simulation or a vanishingly small proportion of advanced computing power is devoted to simulating brains.

Its not hard to imagine why the second option might be true. A society of beings similar to us (but with a much greater technological development) could indeed decide it is not very ethical to simulate beings with enough precision to make them conscious while fooling them and keeping them cut-off from the real world, say Bibeau-Delisle and Brassard.

Another possibility is that advanced civilizations never get to the stage where their technology is powerful enough to perform these kinds of computations. Perhaps they destroy themselves through war or disease or climate change long before then. There is no way of knowing.

But suppose we are in a simulation. Bibeau-Delisle and Brassard ask whether we might escape while somehow hiding our intentions from our overlords. They assume that the simulating technology will be quantum in nature. If quantum phenomena are as difficult to compute on classical systems as we believe them to be, a simulation containing our world would most probably run on quantum computing power, they say.

This raises the possibility that it may be possible to detect our alien overlords since they cannot measure the quantum nature of our world without revealing their presence. Quantum cryptography uses the same principle; indeed, Brassard is one of the pioneers of this technology.

That would seem to make it possible for us to make encrypted plans that are hidden from the overlords, such as secretly transferring ourselves into our own simulations.

However, the overlords have a way to foil this. All they need to do is to rewire their simulation to make it look as if we are able to hide information, even though they are aware of it all the time. If the simulators are particularly angry at our attempted escape, they could also send us to a simulated hell, in which case we would at least have the confirmation we were truly living inside a simulation and our paranoia was not unjustified...conclude Bibeau-Delisle and Brassard, with their tongues firmly in their cheeks.

In that sense, we are the ultimate laboratory guinea pigs: forever trapped and forever fooled by the evil genius of our omnipotent masters.

Time for another game of Civilization VI.

Ref: arxiv.org/abs/2008.09275 : Probability and Consequences of Living Inside a Computer Simulation

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This Equation Calculates The Chances We Live In A Computer Simulation - Discover Magazine

The race for quantum supremacy heated up in June, when Honeywell brought to market the worlds highest performing quantum computer. Honeywell claims it is more accurate (i.e., performs with less errors) than competing systems and that its performance will increase by an order of magnitude each year for the next five years.

Inside the chamber of Honeywells quantum computer

The beauty of quantum computing, says Tony Uttley, President of Honeywell Quantum Solutions, is that once you reach a certain level of accuracy, every time you add a qbit [the basic unit of quantum information] you double the computational capacity. So as the quantum computer scales exponentially, you can scale your problem set exponentially.

Tony Uttley, President, Honeywell Quantum Solutions

Uttley sees three distinct eras in the evolution of quantum computing. Today, we are in the emergent erayou can start to prove what kind of things work, what kind of algorithms show the most promise. For example, the Future Lab for Applied Research and Engineering (FLARE) group of JPMorgan Chase published a paper in June summarizing the results of running on the Honeywell quantum computer complex mathematical calculations used in financial trading applications.

The next era Uttley calls classically impractical, running computations on a quantum computer that typically are not run on todays (classical) computers because they take too long, consume too much power, and cost too much. Crossing the threshold from emergent to classically impractical is not very far away, he asserts, probably sometime in the next 18 to 24 months. This is when you build the trust with the organizations you work with that the answer that is coming from your quantum computer is the correct one, says Uttley.

The companies that understand the potential impact of quantum computing on their industries, are already looking at what it would take to introduce this new computing capability into their existing processes and what they need to adjust or develop from scratch, according to Uttley. These companies will be ready for the shift from emergent to classically impractical which is going to be a binary moment, and they will be able to take advantage of it immediately.

The last stage of the quantum evolution will be classically impossible"you couldnt in the timeframe of the universe do this computation on a classical best-performing supercomputer that you can on a quantum computer, says Uttley. He mentions quantum chemistry, machine learning, optimization challenges (warehouse routing, aircraft maintenance) as applications that will benefit from quantum computing. But what shows the most promise right now are hybrid [resources]you do just one thing, very efficiently, on a quantum computer, and run the other parts of the algorithm or calculation on a classical computer. Uttley predicts that for the foreseeable future we will see co-processing, combining the power of todays computers with the power of emerging quantum computing solutions.

You want to use a quantum computer for the more probabilistic parts [of the algorithm] and a classical computer for the more mundane calculationsthat might reduce the number of qbits needed, explains Gavin Towler, vice president and chief technology officer of Honeywell Performance Materials Technologies. Towler leads R&D activities for three of Honeywell's businesses: Advanced Materials (e.g., refrigerants), UOP (equipment and services for the oil and gas sector), and Process Automation (automation, control systems, software, for all the process industries). As such, he is the poster boy for a quantum computing lead-user.

Gavin Towler, Vice President and Chief Technology Officer, Honeywell Performance Materials and ... [+] Technologies

In the space of materials discovery, quantum computing is going to be critical. Thats not a might or could be. It is going to be the way people do molecular discovery, says Towler. Molecular simulation is used in the design of new molecules, requiring the designer to understand quantum effects. These are intrinsically probabilistic as are quantum computers, Towler explains.

An example he provides is a refrigerant Honeywell produces that is used in automotive air conditioning, supermarkets refrigeration, and homes. As the chlorinated molecules in the refrigerants were causing the hole in the Ozone layer, they were replaced by HFCs which later tuned out to be very potent greenhouse gasses. Honeywell already found a suitable replacement for the refrigerant used in automotive air conditioning, but is searching for similar solutions for other refrigeration applications. Synthesizing in the lab molecules that will prove to have no effect on the Ozone layer or global warming and will not be toxic or flammable is costly. Computer simulation replaces lab work but ideally, you want to have computer models that will screen things out to identify leads much faster, says Towler.

This is where the speed of a quantum computer will make a difference, starting with simple molecules like the ones found in refrigerants or in solvents that are used to remove CO2 from processes prevalent in the oil and gas industry. These are relatively simple molecules, with 10-20 atoms, amenable to be modeled with [todays] quantum computers, says Towler. In the future, he expects more powerful quantum computers to assist in developing vaccines and finding new drugs, polymers, biodegradable plastics, things that contain hundred and thousands of atoms.

There are three ways by which Towlers counterparts in other companies, the lead-users who are interested in experimenting with quantum computing, can currently access Honeywells solution: Run their program directly on Honeywells quantum computer; through Microsoft Azure Quantum services; and working with two startups that Honeywell has invested in, Cambridge Quantum Computing (CQC) and Zapata Computing, both assisting in turning business challenges into quantum computing and hybrid computing algorithms.

Honeywell brings to the quantum computing emerging market a variety of skills in multiple disciplines, with its decades-long experience with precision control systems possibly the most important one. Any at-scale quantum computer becomes a controls problem, says Uttley, and we have experience in some of the most complex systems integration problems in the world. These past experiences have prepared Honeywell to show what quantum computing can do for the world and to rapidly scale-up its solution. Weve built a big auditorium but we are filling out just a few seats right now and we have lots more seats to fill, Uttley sums up this point in time in Honeywells journey to quantum supremacy.

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Honeywell Wants To Show What Quantum Computing Can Do For The World - Forbes

MIP* = RE is not a typo. It is a groundbreaking discovery and the catchy title of a recent paper in the field of quantum complexity theory. Complexity theory is a zoo of complexity classes collections of computational problems of which MIP* and RE are but two.

The 165-page paper shows that these two classes are the same. That may seem like an insignificant detail in an abstract theory without any real-world application. But physicists and mathematicians are flocking to visit the zoo, even though they probably dont understand it all. Because it turns out the discovery has astonishing consequences for their own disciplines.

In 1936, Alan Turing showed that the Halting Problem algorithmically deciding whether a computer program halts or loops forever cannot be solved. Modern computer science was born. Its success made the impression that soon all practical problems would yield to the tremendous power of the computer.

But it soon became apparent that, while some problems can be solved algorithmically, the actual computation will last long after our Sun will have engulfed the computer performing the computation. Figuring out how to solve a problem algorithmically was not enough. It was vital to classify solutions by efficiency. Complexity theory classifies problems according to how hard it is to solve them. The hardness of a problem is measured in terms of how long the computation lasts.

RE stands for problems that can be solved by a computer. It is the zoo. Lets have a look at some subclasses.

The class P consists of problems which a known algorithm can solve quickly (technically, in polynomial time). For instance, multiplying two numbers belongs to P since long multiplication is an efficient algorithm to solve the problem. The problem of finding the prime factors of a number is not known to be in P; the problem can certainly be solved by a computer but no known algorithm can do so efficiently. A related problem, deciding if a given number is a prime, was in similar limbo until 2004 when an efficient algorithm showed that this problem is in P.

Another complexity class is NP. Imagine a maze. Is there a way out of this maze? is a yes/no question. If the answer is yes, then there is a simple way to convince us: simply give us the directions, well follow them, and well find the exit. If the answer is no, however, wed have to traverse the entire maze without ever finding a way out to be convinced.

Such yes/no problems for which, if the answer is yes, we can efficiently demonstrate that, belong to NP. Any solution to a problem serves to convince us of the answer, and so P is contained in NP. Surprisingly, a million dollar question is whether P=NP. Nobody knows.

The classes described so far represent problems faced by a normal computer. But computers are fundamentally changing quantum computers are being developed. But if a new type of computer comes along and claims to solve one of our problems, how can we trust it is correct?

Imagine an interaction between two entities, an interrogator and a prover. In a police interrogation, the prover may be a suspect attempting to prove their innocence. The interrogator must decide whether the prover is sufficiently convincing. There is an imbalance; knowledge-wise the interrogator is in an inferior position.

In complexity theory, the interrogator is the person, with limited computational power, trying to solve the problem. The prover is the new computer, which is assumed to have immense computational power. An interactive proof system is a protocol that the interrogator can use in order to determine, at least with high probability, whether the prover should be believed. By analogy, these are crimes that the police may not be able to solve, but at least innocents can convince the police of their innocence. This is the class IP.

If multiple provers can be interrogated, and the provers are not allowed to coordinate their answers (as is typically the case when the police interrogates multiple suspects), then we get to the class MIP. Such interrogations, via cross examining the provers responses, provide the interrogator with greater power, so MIP contains IP.

Quantum communication is a new form of communication carried out with qubits. Entanglement a quantum feature in which qubits are spookishly entangled, even if separated makes quantum communication fundamentally different to ordinary communication. Allowing the provers of MIP to share an entangled qubit leads to the class MIP*.

It seems obvious that communication between the provers can only serve to help the provers coordinate lies rather than assist the interrogator in discovering truth. For that reason, nobody expected that allowing more communication would make computational problems more reliable and solvable. Surprisingly, we now know that MIP* = RE. This means that quantum communication behaves wildly differently to normal communication.

In the 1970s, Alain Connes formulated what became known as the Connes Embedding Problem. Grossly simplified, this asked whether infinite matrices can be approximated by finite matrices. This new paper has now proved this isnt possible an important finding for pure mathematicians.

In 1993, meanwhile, Boris Tsirelson pinpointed a problem in physics now known as Tsirelsons Problem. This was about two different mathematical formalisms of a single situation in quantum mechanics to date an incredibly successful theory that explains the subatomic world. Being two different descriptions of the same phenomenon it was to be expected that the two formalisms were mathematically equivalent.

But the new paper now shows that they arent. Exactly how they can both still yield the same results and both describe the same physical reality is unknown, but it is why physicists are also suddenly taking an interest.

Time will tell what other unanswered scientific questions will yield to the study of complexity. Undoubtedly, MIP* = RE is a great leap forward.

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Major quantum computational breakthrough is shaking up physics and maths - The Conversation UK