Fewer Qubits Unlock More Powerful Quantum Chemistry Simulations

Scientists at the University College London, Dario Picozzi and Jonathan Tennyson, have developed a new symmetry-adapted qubit encoding with complete active space (SAE-CAS) to enhance quantum chemistry calculations on both fault-tolerant and near-term quantum processors. This method addresses a critical limitation in the field: the substantial qubit requirements for accurate molecular simulations. By extending symmetry-adapted mappings to approximate symmetries, particularly those associated with frozen-core and virtual orbitals, the researchers have achieved a reduction in qubit counts without significant loss of accuracy. Their approach builds upon the established Bravyi-Kitaev mapping and demonstrably reduces both qubit counts and circuit complexity, leading to faster convergence in variational quantum eigensolver (VQE) calculations for small molecules and enabling calculations where previous methods failed. An open-source implementation is provided to facilitate broader adoption and more efficient molecular simulations utilising quantum computers.

Symmetry-adapted qubit encoding dramatically reduces computational cost for accurate molecular simulations

A five-fold reduction in qubit counts has been achieved for molecular simulations, successfully modelling systems where previous methods struggled. Complete active space reference energies were consistently attained in nine small molecules, a significant improvement over the Jordan-Wigner CAS method, which experienced convergence issues in these same instances. The core innovation lies in leveraging the approximate symmetries inherent in molecular systems, specifically those related to frozen-core and virtual orbitals. These symmetries arise from the predictable behaviour of electrons in these orbitals; frozen-core electrons are consistently occupied and do not participate in bonding, while virtual orbitals are consistently unoccupied. By recognising and exploiting these symmetries, the SAE-CAS method minimises the computational resources required without compromising the accuracy of the simulation. This is achieved through an extension of existing symmetry encodings and seamless integration with the Bravyi-Kitaev mapping, a standard technique for representing fermionic operators (electrons) using qubits.

The accompanying open-source implementation, named QuantumSymmetry, promises to accelerate molecular simulations on both current and future quantum computers. Benchmarking across the nine small molecules demonstrated that SAE-CAS consistently matched complete active space reference energies, a benchmark for accuracy in quantum chemistry. In contrast, the Jordan-Wigner CAS method failed to converge for these molecules, highlighting the superiority of the new approach. Furthermore, SAE-CAS lessened the weight of Pauli operators within the quantum circuits. Pauli operators represent the fundamental building blocks of quantum operations, and reducing their weight directly translates to reduced circuit complexity and computational cost. A hardware-efficient shifted-circular-alternating ansatz, when utilised in conjunction with SAE-CAS, resulted in shallower quantum circuits with fewer parameters, further accelerating convergence. The QuantumSymmetry package is freely available, facilitating wider adoption of this approach within the quantum computing community. While current results are limited to relatively small molecules and do not yet demonstrate scalability to systems of practical, pharmaceutical relevance, such as drug discovery or materials science, future work will focus on extending the method to larger, more complex systems and exploring its potential for tackling real-world chemical challenges. The method’s efficacy is particularly notable given the challenges associated with accurately representing electron correlation, a key factor in determining molecular properties.

Reducing qubit requirements enables more complex molecular modelling with near-term quantum devices

Advances in quantum computing are steadily bringing the prospect of accurate molecular simulations closer to reality, but a fundamental obstacle persists: the substantial number of qubits required to accurately model even relatively simple molecules. Current quantum computers are limited in qubit capacity, and the exponential scaling of qubit requirements with system size presents a significant barrier to progress. Therefore, any method demonstrably reducing the demand for qubits represents a genuine and valuable advancement. Symmetry-adapted qubit encoding, combined with complete active space, offers a promising pathway to more efficient molecular modelling, even on the relatively basic hardware available today. Complete active space (CAS) is a highly accurate, but computationally expensive, method for calculating electronic structure, and reducing its resource demands is crucial for practical application.

This optimisation is critically important for extending the reach of quantum simulations beyond trivial examples and towards genuinely useful chemical predictions. The new approach represents a major refinement in how molecular systems are prepared for quantum computation, building upon established techniques by intelligently incorporating approximate symmetries found within molecules. Specifically, it exploits the consistent occupation or lack thereof of certain electron orbitals. By reducing the number of qubits needed to represent these systems, it enables calculations previously unattainable with conventional methods, as demonstrated through the successful modelling of the nine small molecules. This paves the way for investigations into more complex chemical phenomena, such as reaction mechanisms, excited states, and spectroscopic properties. The SAE-CAS method effectively maps the second-quantised Hamiltonian, which describes the electronic structure of the molecule, onto an active-space qubit Hamiltonian, thereby reducing the computational burden. The derivation of this mapping and the proof of its equivalence to the original Hamiltonian are key contributions of this work. Furthermore, the ability to accurately represent molecular systems with fewer qubits has implications for error mitigation strategies, as fewer qubits mean fewer opportunities for errors to accumulate during the computation. This research contributes to the broader effort of harnessing the power of quantum computers to solve challenging problems in chemistry and materials science.

The researchers developed a symmetry-adapted qubit encoding with complete active space, a method for reducing the number of qubits required for quantum chemistry calculations. This is important because it allows for more complex molecular simulations to be performed with existing and near-term quantum processors. Benchmarking on nine small molecules demonstrated that this approach reduces qubit counts and circuit complexity, often achieving convergence where standard methods failed. The authors have made their implementation openly available as a Python package called QuantumSymmetry, facilitating further research in this area.