laboratory and into the laptops of developers - Open Source (Apache 2.0) - Written in Python - Modular and extensible Terra A solid foundation for quantum computing Aqua Algorithms for near-term quantum applications Ignis Compute in the presence of errors Aer A high performance simulator framework for quantum circuits 0.7 0.4 0.1 0.1
Laboratory NC State University The University of Melbourne University of Munich Keio University Partners JP Morgan Chase & Co. Samsung Daimler JSR Corporation Accenture ExxonMobil Academia University of Minho MIT EDX.org Virginia Tech National University of Taiwan CERN University of Montpellier Members Honda Barclays Hitachi Metals Nagase Mizuho MUFG Mitsubishi Chemicals Argonne Fermilab Berkeley Lab Startups QC Ware CQC 1QBit Zapata QXBranch Q-CTRL Quantum Benchmark MDR Qu&Co Labber Quantum JoS Quantum SolidStateAI Max Kelsen Strangeworks Boxcat ProteinQure Netramark Entropica Labs IBM Q Network
using optimization Experimental evaluation of ground state properties of small molecules on real hardware Impacts Efficiently compute reaction rates of chemical processes Speed up molecular discovery, materials research, drug design Path to quantum advantage Evaluate properties of molecules that cannot be simulated on classical computer Challenges Larger molecules require larger-depth circuits. Develop new algorithms for evaluating properties. Scale up number of qubits in hardware. Quantum chemistry is a natural application for near-term quantum computers.
Experimentally implemented binary classification using a quantum support vector machine Use “quantum kernels” in classical machine learning algorithms for classification Use quantum circuits directly for classifying data New feature maps that cannot be efficiently simulated classically Classification with higher-dimensional data sets Extend technique(s) to more qubits Improved error mitigation to scale up qubit count Methods for selecting quantum feature maps on practical datasets Quantum machine learning: trendy buzzword, or useful near-term application?
in superposition. Train the quantum classifier by updating the quantum network. Using entanglement, map the training data to a quantum state. 0 1 2 0 1 2 1 2 0
Monte Carlo analysis can be sped up using a quantum computer. Improve convergence of simulations Decrease time-to- solution for the analysis Proof-of-principle demonstration for two (financial) toy models Analyzed toy models using real hardware. Leverages an existing quantum algorithm with known speedup Algorithms: Reduce required circuit depth Hardware: Increase depth by increasing the coherence times and reducing error rates
the amplitude estimation algorithm. Example: computing expected value of a T-bill (single-period binomial-tree model) Monte Carlo sampling SWAP gate Inverse Quantum Fourier transform Quadratic improvement compared to classical Monte Carlo methods
using real machines today Qiskit Learn about and start using Qiskit software development kit IBM Q Network Collaborate, research, and start quantum computing through the IBM Q Network IBM Q Discover quantum computing with IBM Q, IBM’s quantum computing initiative Start your quantum journey today with IBM Q
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