49 research outputs found
Numerical circuit synthesis and compilation for multi-state preparation
Near-term quantum computers have significant error rates and short coherence
times, so compilation of circuits to be as short as possible is essential. Two
types of compilation problems are typically considered: circuits to prepare a
given state from a fixed input state, called "state preparation"; and circuits
to implement a given unitary operation, for example by "unitary synthesis". In
this paper we solve a more general problem: the transformation of a set of
states to another set of states, which we call "multi-state preparation".
State preparation and unitary synthesis are special cases; for state
preparation, , while for unitary synthesis, is the dimension of the
full Hilbert space. We generate and optimize circuits for multi-state
preparation numerically. In cases where a top-down approach based on matrix
decompositions is also possible, our method finds circuits with substantially
(up to 40%) fewer two-qubit gates. We discuss possible applications, including
efficient preparation of macroscopic superposition ("cat") states and synthesis
of quantum channels.Comment: v2: Added to discussion in Sections IIA and VIB; v1: 10 pages, 2
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QFAST: Conflating Search and Numerical Optimization for Scalable Quantum Circuit Synthesis
We present a quantum synthesis algorithm designed to produce short circuits
and to scale well in practice. The main contribution is a novel representation
of circuits able to encode placement and topology using generic "gates", which
allows the QFAST algorithm to replace expensive searches over circuit
structures with few steps of numerical optimization. When compared against
optimal depth, search based state-of-the-art techniques, QFAST produces
comparable results: 1.19x longer circuits up to four qubits, with an increase
in compilation speed of 3.6x. In addition, QFAST scales up to seven qubits.
When compared with the state-of-the-art "rule" based decomposition techniques
in Qiskit, QFAST produces circuits shorter by up to two orders of magnitude
(331x), albeit 5.6x slower. We also demonstrate the composability with other
techniques and the tunability of our formulation in terms of circuit depth and
running time
Improving Quantum Circuit Synthesis with Machine Learning
In the Noisy Intermediate Scale Quantum (NISQ) era, finding implementations
of quantum algorithms that minimize the number of expensive and error prone
multi-qubit gates is vital to ensure computations produce meaningful outputs.
Unitary synthesis, the process of finding a quantum circuit that implements
some target unitary matrix, is able to solve this problem optimally in many
cases. However, current bottom-up unitary synthesis algorithms are limited by
their exponentially growing run times. We show how applying machine learning to
unitary datasets permits drastic speedups for synthesis algorithms. This paper
presents QSeed, a seeded synthesis algorithm that employs a learned model to
quickly propose resource efficient circuit implementations of unitaries. QSeed
maintains low gate counts and offers a speedup of in synthesis time
over the state of the art for a 64 qubit modular exponentiation circuit, a core
component in Shor's factoring algorithm. QSeed's performance improvements also
generalize to families of circuits not seen during the training process.Comment: 11 pages, 10 figure
Powerful Quantum Circuit Resizing with Resource Efficient Synthesis
In the noisy intermediate-scale quantum era, mid-circuit measurement and
reset operations facilitate novel circuit optimization strategies by reducing a
circuit's qubit count in a method called resizing. This paper introduces two
such algorithms. The first one leverages gate-dependency rules to reduce qubit
count by 61.6% or 45.3% when optimizing depth as well. Based on numerical
instantiation and synthesis, the second algorithm finds resizing opportunities
in previously unresizable circuits via dependency rules and other
state-of-the-art tools. This resizing algorithm reduces qubit count by 20.7% on
average for these previously impossible-to-resize circuits
Superstaq: Deep Optimization of Quantum Programs
We describe Superstaq, a quantum software platform that optimizes the
execution of quantum programs by tailoring to underlying hardware primitives.
For benchmarks such as the Bernstein-Vazirani algorithm and the Qubit Coupled
Cluster chemistry method, we find that deep optimization can improve program
execution performance by at least 10x compared to prevailing state-of-the-art
compilers. To highlight the versatility of our approach, we present results
from several hardware platforms: superconducting qubits (AQT @ LBNL, IBM
Quantum, Rigetti), trapped ions (QSCOUT), and neutral atoms (Infleqtion).
Across all platforms, we demonstrate new levels of performance and new
capabilities that are enabled by deeper integration between quantum programs
and the device physics of hardware.Comment: Appearing in IEEE QCE 2023 (Quantum Week) conferenc
Down selecting adjuvanted vaccine formulations: a comparative method for harmonized evaluation.
The need for rapid and accurate comparison of panels of adjuvanted vaccine formulations and subsequent rational down selection, presents several challenges for modern vaccine development. Here we describe a method which may enable vaccine and adjuvant developers to compare antigen/adjuvant combinations in a harmonized fashion. Three reference antigens: Plasmodium falciparum apical membrane antigen 1 (AMA1), hepatitis B virus surface antigen (HBsAg), and Mycobacterium tuberculosis antigen 85A (Ag85A), were selected as model antigens and were each formulated with three adjuvants: aluminium oxyhydroxide, squalene-in-water emulsion, and a liposome formulation mixed with the purified saponin fraction QS21.
The nine antigen/adjuvant formulations were assessed for stability and immunogenicity in mice in order to provide benchmarks against which other formulations could be compared, in order to assist subsequent down selection of adjuvanted vaccines. Furthermore, mouse cellular immune responses were analyzed by measuring IFN-γ and IL-5 production in splenocytes by ELISPOT, and humoral responses were determined by antigen-specific ELISA, where levels of total IgG, IgG1, IgG2b and IgG2c in serum samples were determined.
The reference antigens and adjuvants described in this study, which span a spectrum of immune responses, are of potential use as tools to act as points of reference in vaccine development studies. The harmonized methodology described herein may be used as a tool for adjuvant/antigen comparison studies
Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease
Background: Experimental and clinical data suggest that reducing inflammation without affecting lipid levels may reduce the risk of cardiovascular disease. Yet, the inflammatory hypothesis of atherothrombosis has remained unproved. Methods: We conducted a randomized, double-blind trial of canakinumab, a therapeutic monoclonal antibody targeting interleukin-1β, involving 10,061 patients with previous myocardial infarction and a high-sensitivity C-reactive protein level of 2 mg or more per liter. The trial compared three doses of canakinumab (50 mg, 150 mg, and 300 mg, administered subcutaneously every 3 months) with placebo. The primary efficacy end point was nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death. RESULTS: At 48 months, the median reduction from baseline in the high-sensitivity C-reactive protein level was 26 percentage points greater in the group that received the 50-mg dose of canakinumab, 37 percentage points greater in the 150-mg group, and 41 percentage points greater in the 300-mg group than in the placebo group. Canakinumab did not reduce lipid levels from baseline. At a median follow-up of 3.7 years, the incidence rate for the primary end point was 4.50 events per 100 person-years in the placebo group, 4.11 events per 100 person-years in the 50-mg group, 3.86 events per 100 person-years in the 150-mg group, and 3.90 events per 100 person-years in the 300-mg group. The hazard ratios as compared with placebo were as follows: in the 50-mg group, 0.93 (95% confidence interval [CI], 0.80 to 1.07; P = 0.30); in the 150-mg group, 0.85 (95% CI, 0.74 to 0.98; P = 0.021); and in the 300-mg group, 0.86 (95% CI, 0.75 to 0.99; P = 0.031). The 150-mg dose, but not the other doses, met the prespecified multiplicity-adjusted threshold for statistical significance for the primary end point and the secondary end point that additionally included hospitalization for unstable angina that led to urgent revascularization (hazard ratio vs. placebo, 0.83; 95% CI, 0.73 to 0.95; P = 0.005). Canakinumab was associated with a higher incidence of fatal infection than was placebo. There was no significant difference in all-cause mortality (hazard ratio for all canakinumab doses vs. placebo, 0.94; 95% CI, 0.83 to 1.06; P = 0.31). Conclusions: Antiinflammatory therapy targeting the interleukin-1β innate immunity pathway with canakinumab at a dose of 150 mg every 3 months led to a significantly lower rate of recurrent cardiovascular events than placebo, independent of lipid-level lowering. (Funded by Novartis; CANTOS ClinicalTrials.gov number, NCT01327846.
Pooled analysis of WHO Surgical Safety Checklist use and mortality after emergency laparotomy
Background The World Health Organization (WHO) Surgical Safety Checklist has fostered safe practice for 10 years, yet its place in emergency surgery has not been assessed on a global scale. The aim of this study was to evaluate reported checklist use in emergency settings and examine the relationship with perioperative mortality in patients who had emergency laparotomy. Methods In two multinational cohort studies, adults undergoing emergency laparotomy were compared with those having elective gastrointestinal surgery. Relationships between reported checklist use and mortality were determined using multivariable logistic regression and bootstrapped simulation. Results Of 12 296 patients included from 76 countries, 4843 underwent emergency laparotomy. After adjusting for patient and disease factors, checklist use before emergency laparotomy was more common in countries with a high Human Development Index (HDI) (2455 of 2741, 89.6 per cent) compared with that in countries with a middle (753 of 1242, 60.6 per cent; odds ratio (OR) 0.17, 95 per cent c.i. 0.14 to 0.21, P <0001) or low (363 of 860, 422 per cent; OR 008, 007 to 010, P <0.001) HDI. Checklist use was less common in elective surgery than for emergency laparotomy in high-HDI countries (risk difference -94 (95 per cent c.i. -11.9 to -6.9) per cent; P <0001), but the relationship was reversed in low-HDI countries (+121 (+7.0 to +173) per cent; P <0001). In multivariable models, checklist use was associated with a lower 30-day perioperative mortality (OR 0.60, 0.50 to 073; P <0.001). The greatest absolute benefit was seen for emergency surgery in low- and middle-HDI countries. Conclusion Checklist use in emergency laparotomy was associated with a significantly lower perioperative mortality rate. Checklist use in low-HDI countries was half that in high-HDI countries.Peer reviewe