Rhonda Germany Ballintyn joins our Board
We are excited to announce the appointment of marketing veteran Rhonda Germany Ballintyn as a board member. Most recently, Germany Ballintyn was the corporate vice president, chief strategy and marketing officer at Honeywell International until her retirement in 2017.
Raising $21 Million To Bring Quantum Computing To Enterprise Applications
Today, we announced that we have raised $21 million in Series A financing. Led by Comcast Ventures and Prelude Ventures, the round includes participation from new and existing investors Pitango Ventures, BASF Venture Capital, Robert Bosch Venture Capital, Pillar VC, and The Engine.
View the article on Forbes.
An automated tool for building better quantum circuits.
Compressed Unsupervised State Preparation (CUSP) is a method for building more efficient quantum circuits by using a quantum autoencoder.
View our example code.
What is CUSP?
Compressed Unsupervised State Preparation (CUSP) is a method for building more efficient quantum circuits by using a quantum autoencoder. The protocol performs a kind of circuit synthesis that, if training is successful, results in a more compact circuit. Since typically shorter-depth circuits are less prone to noise on a real quantum computer, this tool gives the opportunity to make a more accurate state preparation, resulting in better accuracy for a quantum computation. In this demo, we will use the example of improving a circuit which computes the ground state energies of molecular hydrogen at various bond lengths.
Zapata designed, from the ground up, CUSP — a quantum machine learning algorithm for building more efficient quantum circuits — and implemented it for Google’s quantum computers. CUSP draws upon the research by co-founder Alán Aspuru-Guzik, a pioneer in quantum simulation for chemistry and materials. CUSP is an important tool when a quantum algorithm is too large to optimize by hand as it can automatically compress a computation to fit on near term quantum computers. It is one of the many powerful, hardware-agnostic algorithms that Zapata is currently developing for Fortune 1000 companies in markets such as finance, pharmaceuticals, and materials. Zapata’s algorithms can run on the latest quantum hardware made by Google and other companies in this field. The USP algorithm and others like it will hasten breakthroughs and enable the next generation of discoveries in chemistry, materials, and artificial intelligence.