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Where and when to use a quantum computer? Its one of the most common questions that experts, such as Kirk Bresniker Chief Architect at Hewlett Packard Labs, get asked by business leaders. Enterprises want to know where in the IT portfolio quantum computers will bring the most significant rewards and when is the right time for firms to invest in solutions.

For decades, quantum computing developers have been promising big things from quantum computers, which is understandable. Quantum computers are costly to develop, and being modest about the technology isnt going to win over investors. However, its important to note that quantum computers arent universal computing devices.

Quantum computing promises transformational gains for solving some problems, but little or none for others, write MIT Sloan School of Management researchers in a paper dubbed The Quantum Tortoise and the Classical Hare submitted to arXiv.

The team, led by Neil Thompson whose career includes appointments at Lawrence Livermore National Laboratories, Bain and Company, The United Nations, the World Bank, and the Canadian Parliament has come up with a simple framework for understanding which problems quantum computing will accelerate (and which it will not).

Quantum computers open the door to probabilistic computing, with quantum gates adding a twist to each of the qubits in the calculation. As the system evolves, the qubits interact and point to the most likely solution to the problem that theyve been arranged to describe.

Prodding a bit further, if we consider classical machines as mapping business questions onto maths a perspective shared by Scott Buchholz, Global Quantum Lead at Deloitte Consulting, at this years D-Wave Qubits conference then quantum computers give us the chance to use physics instead.

It turns out that some questions are easier to map onto physics than others, and this gets to one of the key considerations in the MIT framework on where and when to use a quantum computer.

Much of the talk on progress in quantum computing surrounds the number of qubits. Systems are notoriously noisy, which adds to the number of physical qubits that are required to facilitate error correction on logical qubits. On top of this, there are multiple ways of engineering the superposition of ones and zeros through the use of superconducting, trapped ion, photonic, or silicon spin qubits.

Each quantum computing developer has its own preferred approach, and as you walk down the path of trying to understand how quantum computing works, the discussion becomes one about the technology. And this is fine. Large companies can engage their R&D teams and have conversations with hardware developers.

However, just as you dont need to understand whats happening inside a CPU to benefit from a laptop, companies can focus their attention on the kinds of problems that quantum computers can help with, rather than getting bogged down with the numbers and types of qubits.

In their decision-making framework, Thompson and his colleagues identify two determinants in understanding when to use a quantum computer the efficiency of the algorithm and the scale of the problem that needs to be solved.

The problem size matters because the benefit of an algorithmic advantage is larger for larger problems, explains the team. This means that if a problem is too small, the classical computer will have already completed the problem by the time the quantum computers algorithmic benefit kicks in.

Quantum computers are often mentioned in terms of being able to tackle problems that are effectively impossible with classical machines. But the researchers want to guide enterprises on other opportunities too, where a quantum economic advantage exists.

Their analysis also considers technology roadmaps so that companies can assess when the window for using a quantum computer could open up for them.

Problems that become exponentially harder to solve as the size of the problem increases are interesting candidates when thinking about alternatives to using classical computing machines. And Thompson and his co-authors Sukwoong Choi and William Moses provide a useful rule of thumb.

If a classical algorithm takes exponential time and there exists a polynomial quantum algorithm, youre likely to get a speedup, they comment when discussing their framework on when to use a quantum computer.

Its worth pointing out that companies dont have to invest in bare metal hardware. For most customers, their first experience of what qubits are capable of will be via the cloud using one of a number of QCaaS providers.

Amazon Braket makes it straightforward for firms to work with different types of quantum computers and circuit simulators. Amazon advertises that Braket comes with one free hour of simulation time per month, lowering the cost barrier to getting started.

QCaaS hardware associated with Bracket includes gate-based superconducting processors from Rigetti and OQC, neutral atom-based quantum processors from QuEra, and IONQs gate-based ion-trap processors.

Microsofts Azure Quantum cloud service is another option for firms. Here, users get access to systems from Quantinuum, QCI, and PASQAL, as well as the quantum computing hardware mentioned above.

And companies can also access quantum computing solutions in the cloud using QCaaS platforms operated by developers such as IBM, Google, and D-Wave.

Theres no shortage of options, and with frameworks to guide enterprises on where and when to use a quantum computer, now is a good time to think about the types of algorithms supporting your operations and whether qubits can provide an economic advantage to the bottom line.

Excerpt from:
Tips on where and when to use a quantum computer - TechHQ