Towards near-term quantum simulation of materials.

Determining the ground and excited state properties of materials is considered one of the most promising applications of quantum computers. On near-term hardware, the limiting constraint on such simulations is the requisite circuit depths and qubit numbers, which currently lie well beyond near-term capabilities. Here we develop a quantum algorithm which reduces the estimated cost of material simulations. For example, we obtain a circuit depth improvement by up to 6 orders of magnitude for a Trotter layer of time-dynamics simulation in the transition-metal oxide SrVO3 compared with the best previous quantum algorithms. We achieve this by introducing a collection of connected techniques, including highly localised and physically compact representations of materials Hamiltonians in the Wannier basis, a hybrid fermion-to-qubit mapping, and an efficient circuit compiler. Combined together, these methods leverage locality of materials Hamiltonians and result in a design that generates quantum circuits with depth independent of the system's size. Although the requisite resources for the quantum simulation of materials are still beyond current hardware, our results show that realistic simulation of specific properties may be feasible without necessarily requiring fully scalable, fault-tolerant quantum computers, providing quantum algorithm design incorporates deeper understanding of the target materials and applications.

Hamiltonian simulation algorithms for near-term quantum hardware.

The quantum circuit model is the de-facto way of designing quantum algorithms. Yet any level of abstraction away from the underlying hardware incurs overhead. In this work, we develop quantum algorithms for Hamiltonian simulation "one level below" the circuit model, exploiting the underlying control over qubit interactions available in most quantum hardware and deriving analytic circuit identities for synthesising multi-qubit evolutions from two-qubit interactions. We then analyse the impact of these techniques under the standard error model where errors occur per gate, and an error model with a constant error rate per unit time. To quantify the benefits of this approach, we apply it to time-dynamics simulation of the 2D spin Fermi-Hubbard model. Combined with new error bounds for Trotter product formulas tailored to the non-asymptotic regime and an analysis of error propagation, we find that e.g. for a 5 × 5 Fermi-Hubbard lattice we reduce the circuit depth from 1, 243, 586 using the best previous fermion encoding and error bounds in the literature, to 3, 209 in the per-gate error model, or the circuit-depth-equivalent to 259 in the per-time error model. This brings Hamiltonian simulation, previously beyond reach of current hardware for non-trivial examples, significantly closer to being feasible in the NISQ era.

The impact of antibody selection on the detection of cardiac troponin I.

BACKGROUND: Cardiac troponin I (cTnI) is the current standard biomarker for diagnosing acute myocardial infarction and for risk-stratification of acute coronary syndromes in patients. However, it remains unclear how the epitope specificity of antibodies in immunoassays influences the detection of various modified forms of cTnI. METHODS: Four mouse anti-human cTnI monoclonal antibodies targeting different regions of human cTnI were chosen for immunoaffinity purification of cTnI from human and swine cardiac tissue. High-resolution intact protein mass spectrometry was employed to assess the comparative performance of these four antibodies in detecting modified forms of cTnI. RESULTS: Our data revealed that antibody selection significantly impacts the relative protein yield of cTn from immunoaffinity purification. Remarkably, a single amino acid variation in cTnI (G->S) in the epitope region completely abolished the binding between monoclonal antibody 560 and swine cTnI in solution. Moreover, proteolytic degradation around the epitope region severely compromised the detection of proteolytic fragment forms of cTnI by monoclonal antibodies. In contrast, the phosphorylation status near the epitope region did not significantly affect the antibody recognition of cTnI. CONCLUSION: Caution needs to be taken in the interpretation of the data produced by immuno-assays with monoclonal antibodies against various epitopes of cTnI.