Abstract
A number of commercially available quantum computers, such as those based on trapped-ion or superconducting qubits, can now perform mid-circuit measurements and resets. In addition to being crucial for quantum error correction, this capability can help reduce the number of qubits needed to execute many types of quantum algorithms by measuring qubits as early as possible, resetting them, and reusing them elsewhere in the circuit. In this work, we introduce the idea of qubit-reuse compilation, which takes as input a quantum circuit and produces as output a compiled circuit that requires fewer qubits to execute due to qubit reuse. We present two algorithms for performing qubit-reuse compilation: an exact constraint programming optimization model and a greedy heuristic. We introduce the concept of dual circuits, obtained by exchanging state preparations with measurements and vice versa and reversing time, and show that optimal qubit-reuse compilation requires the same number of qubits to execute a circuit as its dual. We illustrate the performance of these algorithms on a variety of relevant near-term quantum circuits, such as one-dimensional and two-dimensional time-evolution circuits, and numerically benchmark their performance on the quantum adiabatic optimization algorithm (QAOA) applied to the MaxCut problem on random three-regular graphs. To demonstrate the practical benefit of these techniques, we experimentally realize an 80-qubit QAOA MaxCut circuit on the 20-qubit Quantinuum H1-1 trapped-ion quantum processor using qubit-reuse compilation algorithms.
- Received 7 April 2023
- Revised 18 August 2023
- Accepted 1 November 2023
DOI:https://doi.org/10.1103/PhysRevX.13.041057
Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.
Published by the American Physical Society
Physics Subject Headings (PhySH)
Popular Summary
Commercial quantum computers are limited in the number of qubits available to perform algorithms, which restricts the size and complexity of problems they can solve. This work presents a technique for solving large problems on small quantum computers by compressing a quantum program to fit in a small number of qubits. We accomplish this compression using a recently developed hardware capability to measure, reset, and reuse qubits in the middle of a program, so that each physical qubit can serve the role of multiple qubits in the original program. We develop algorithms for calculating the minimum number of qubits required to run a program and automate the procedure of compressing a program down to that number of qubits.
The key ingredient to these algorithms is the fact that only a small part of a program needs to be implemented to measure one output qubit: the part that causally influences that output. We test these algorithms on several relevant and interesting problems by hand calculation, numerical computation, and finally by experimental demonstration in which an 80-qubit program was implemented on the 20-qubit Quantinuum H1-1 trapped-ion quantum computer.
Ultimately, this work pushes current quantum computing technology closer to accomplishing meaningful physical tasks that cannot be performed classically, like computing correlation functions to extract critical properties of large quantum systems near phase transitions.
