In this chapter a quantum music generation application called QuiKo will be discussed. It combines existing quantum algorithms with data encoding methods from quantum machine learning to build drum and audio sample patterns from a database of audio tracks. QuiKo leverages the physical properties and characteristics of quantum computers to generate what can be referred to as Soft Rules proposed by Alexis Kirke. These rules take advantage of the noise produced by quantum devices to develop flexible rules and grammars for quantum music generation. These properties include qubit decoherence and phase kickback due controlled quantum gates within the quantum circuit. QuiKo builds upon the concept of soft rules in quantum music generation and takes it a step further. It attempts to mimic and react to an external musical inputs, similar to the way that human musicians play and compose with one another. Audio signals are used as inputs into the system. Feature extraction is then performed on the signal to identify the harmonic and percussive elements. This information is then encoded onto the quantum circuit. Measurements of the quantum circuit are then taken providing results in the form of probability distributions for external music applications to use to build the new drum patterns.
翻译:本章将讨论名为 QuiKo 的量子音乐生成应用程序。 它将现有的量子算法与量子机器学习的数据编码方法结合起来, 以从音轨数据库中建立鼓和音频样本模式。 QuiKo 利用量子计算机的物理属性和特性来产生亚历克西斯·柯克特提议的“ Soft Rules” 。 这些规则利用量子设备产生的噪音来为量子音乐生成制定灵活的规则和语法。 这些特性包括量子电路中的qubit decoherence 和 相继相继回回回回调控制量子门。 QuiKo 以量子机器生成的软性规则概念为基础, 并更进一步。 它试图模拟和反应外部的音乐投入, 类似人类音乐家玩耍和相互组合的方式。 音频信号被作为系统的投入。 然后在信号上进行地形提取, 以辨别调和感应元素。 这些信息随后被编码为量子电路。 量子电路的测量结果以外部音乐应用模式的概率分布形式提供结果。