When Peter Shor, MIT professor and now member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), first demonstrated the ability of quantum computers to solve problems faster than classical computers, he inspired scientists to use the emerging technology. Inspired to imagine countless possibilities. However, thirty years later, the quantum edge remains a pinnacle that has yet to be reached.

Unfortunately, the technology of quantum computing is not yet fully operational. A major challenge lies in translating quantum algorithms from abstract mathematical concepts into concrete code that can run on quantum computers. Whereas programmers for regular computers have access to a myriad of languages, such as Python and C++, that align with standard classical computing abstractions, quantum programmers have no such luxury; Few quantum programming languages exist today, and they are comparatively difficult to use because quantum computing abstractions are still in flux. In their recent work, the MIT researchers highlighted that this asymmetry exists because quantum computers do not follow the same rules for completing each step of a program in order – a necessary process for all computers that need to be controlled. is called flow – and present a new abstract model for a quantum computer that might be easier to program.

one in paper The group, soon to be presented at the ACM conference on Object-Oriented Programming, Systems, Languages, and Applications, outlines a new conceptual model for a quantum computer, called a quantum control machine, that would allow us to make it easier to program. Can bring closer. Write like regular classical computers. Such a feat would help turbocharge tasks that are impossible for regular computers to accomplish efficiently, such as factoring large numbers, retrieving information in databases and simulating how molecules interact for drug discoveries.

“Our work presents the principles that govern how you can and cannot correctly program a quantum computer,” says lead author and CSAIL PhD student Charles Yuan SM ’22. “One of these laws implies that if you try to program a quantum computer using the same basic instructions as a regular classical computer, you will turn that quantum computer into a classical computer and get the same performance benefits. Will lose. These laws explain why quantum programming languages are difficult to design and tell us how to improve them.

**old school vs new school computing**

One reason why classical computers are relatively easy to program today is that their control flow is fairly straightforward. The basic ingredients of a classical computer are simple: a simple collection of binary digits, or bits, zeros and ones. These materials are collected into instructions and components of the computer’s architecture. An important component is the program counter, which locates the next instruction in the program, like a chef following a recipe by remembering the next direction from memory. As the algorithm navigates sequentially through the program, a control flow instruction called a conditional jump updates the program counter so that the computer can either proceed to the next instruction or deviate from its current steps.

In contrast, the basic component of a quantum computer is a qubit, which is the quantum version of a bit. This quantum data exists in a state of zero and one at the same time, which is known as superposition. Based on this idea, a quantum algorithm can choose to execute a superposition of two instructions at the same time – a concept called quantum control flow.

The problem is that current designs of quantum computers do not include a program counter or the equivalent of conditional jumps. In practice, this means that programmers usually implement control flow by manually arranging logical gates that describe the computer’s hardware, which is a difficult and error-prone process. To provide these features and bridge the gap with classical computers, Yuan and his co-authors created the quantum control machine – an instruction set for quantum computers that works like the classical idea of a virtual machine. In their paper, the researchers imagine how programmers could use this instruction set to implement quantum algorithms for problems such as factoring numbers and simulating chemical interactions.

As a technical summary of this work, the researchers prove that a quantum computer cannot support the same conditional jump instruction as a classical computer, and show how to modify it to work correctly on a quantum computer. Go. In particular, a quantum control machine has instructions that are all reversible – they can run both forward and backward in time. A quantum algorithm needs to reverse all instructions, including control flow, so that it can process quantum information without accidentally destroying its superposition and generating incorrect answers.

**The hidden simplicity of quantum computers**

According to Yuan, you don’t need to be a physicist or a mathematician to understand how this futuristic technology works. He says that quantum computers are not necessarily mysterious machines that require scary equations to understand. With the Quantum Control Machine, the CSAIL team aims to lower the barrier of entry for people to interact with quantum computers by elevating the unfamiliar concept of quantum control flow to a level that mirrors the familiar concept of control flow in classical computers. Is. By highlighting the do’s and don’ts of building and programming a quantum computer, they hope to educate people outside the field about the power of quantum technology and its ultimate limits.

Nevertheless, the researchers caution that as is the case with many other designs, it is not yet possible to directly translate their work into a practical hardware quantum computer due to the limitations of today’s qubit technology. Their goal is to develop ways to implement more types of quantum algorithms as programs that make efficient use of a limited number of qubits and logic gates. Doing so will bring us closer to running these algorithms on quantum computers that may come online in the near future.

“The fundamental capabilities of models of quantum computation have been a central discussion in quantum computation theory since the beginning,” says Patrick Rall, a researcher at the MIT-IBM Watson AI Lab who was not involved in the paper. “The earliest models of these are quantum Turing machines capable of quantum control flow. However, the field has largely moved on to simpler and more convenient circuit models, which lack control flow in quantum. Yuan, Villani, and Carbin successfully capture the underlying cause of this transition by using the perspective of programming languages. While control flow is central to our understanding of classical computation, quantum is completely different! I expect this observation to be important for the design of modern quantum software frameworks as hardware platforms become more mature.

The paper lists two additional CSAIL members as authors: PhD student Aggie Villani ’21 and Associate Professor Michael Carbin. His work was supported, in part, by the National Science Foundation and the Sloan Foundation.

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